



'''simple docstring'''






import unittest

from transformers import CamembertTokenizer, CamembertTokenizerFast
from transformers.testing_utils import get_tests_dir, require_sentencepiece, require_tokenizers, slow
from transformers.utils import is_torch_available

from ...test_tokenization_common import TokenizerTesterMixin


lowerCAmelCase:   Dict					=		get_tests_dir('fixtures/test_sentencepiece.model')
lowerCAmelCase:   Any					=		get_tests_dir('fixtures/test_sentencepiece_bpe.model')

lowerCAmelCase:   Optional[int]					=		'pt' if is_torch_available() else 'tf'

@require_sentencepiece
@require_tokenizers
class 				a__(   lowerCamelCase__						,					unittest.TestCase      ):
    lowercase__					=       CamembertTokenizer
    lowercase__					=       CamembertTokenizerFast
    lowercase__					=       True
    lowercase__					=       True







    def      lowercase_			( self					:    int      ):
      super().setUp()

      # We have a SentencePiece fixture for testing
      a		:   Optional[int]	  =	CamembertTokenizer(__snake_case      )
      tokenizer.save_pretrained(self.tmpdirname      )







    def      lowercase_			( self					:    int      ):
      a		:   str	  =	'<pad>'
      a		:   Tuple	  =	1

      self.assertEqual(self.get_tokenizer()._convert_token_to_id(__snake_case      )		,     __snake_case      )
      self.assertEqual(self.get_tokenizer()._convert_id_to_token(__snake_case      )		,     __snake_case      )







    def      lowercase_			( self					:    Optional[int]      ):
      a		:   Union[str, Any]	  =	list(self.get_tokenizer().get_vocab().keys()      )

      self.assertEqual(vocab_keys[0]		,     '<s>NOTUSED'      )
      self.assertEqual(vocab_keys[1]		,     '<pad>'      )
      self.assertEqual(vocab_keys[-1]		,     '<mask>'      )
      self.assertEqual(len(__snake_case      )		,     10_04      )







    def      lowercase_			( self					:    Union[str, Any]      ):
      self.assertEqual(self.get_tokenizer().vocab_size		,     10_05      )







    def      lowercase_			( self					:    Tuple      ):
      a		:   List[str]	  =	CamembertTokenizer(__snake_case      )
      tokenizer.save_pretrained(self.tmpdirname      )
      a		:   Dict	  =	CamembertTokenizerFast.from_pretrained(self.tmpdirname      )

      a		:   List[Any]	  =	'I was born in 92000, and this is falsé.'

      a		:   Dict	  =	tokenizer.encode(__snake_case      )
      a		:   Dict	  =	rust_tokenizer.encode(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   Union[str, Any]	  =	tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      a		:   Optional[int]	  =	rust_tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      # <unk> tokens are not the same for `rust` than for `slow`.
      # Because spm gives back raw token instead of `unk` in EncodeAsPieces
      # tokens = tokenizer.tokenize(sequence)
      a		:   Optional[int]	  =	tokenizer.convert_ids_to_tokens(__snake_case      )
      a		:   List[Any]	  =	rust_tokenizer.tokenize(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )







    def      lowercase_			( self					:    Any      ):
      if not self.test_rust_tokenizer:
        return

      a		:   Optional[int]	  =	self.get_tokenizer()
      a		:   Dict	  =	self.get_rust_tokenizer()

      a		:   Optional[int]	  =	'I was born in 92000, and this is falsé.'

      a		:   Tuple	  =	tokenizer.tokenize(__snake_case      )
      a		:   List[Any]	  =	rust_tokenizer.tokenize(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   List[str]	  =	tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      a		:   Dict	  =	rust_tokenizer.encode(__snake_case		,     add_special_tokens=__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )

      a		:   Dict	  =	self.get_rust_tokenizer()
      a		:   Optional[int]	  =	tokenizer.encode(__snake_case      )
      a		:   List[str]	  =	rust_tokenizer.encode(__snake_case      )
      self.assertListEqual(__snake_case		,     __snake_case      )







    @slow
    def      lowercase_			( self					:    int      ):
      # fmt: off
      a		:   Union[str, Any]	  =	{'input_ids': [[5, 54, 71_96, 2_97, 30, 23, 7_76, 18, 11, 32_15, 37_05, 82_52, 22, 31_64, 11_81, 21_16, 29, 16, 8_13, 25, 7_91, 33_14, 20, 34_46, 38, 2_75_75, 1_20, 6, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1], [5, 4_68, 17, 11, 90_88, 20, 15_17, 8, 2_28_04, 1_88_18, 10, 38, 6_29, 6_07, 6_07, 1_42, 19, 71_96, 8_67, 56, 1_03_26, 24, 22_67, 20, 4_16, 50_72, 1_56_12, 2_33, 7_34, 7, 23_99, 27, 16, 30_15, 16_49, 7, 24, 20, 43_38, 23_99, 27, 13, 34_00, 14, 13, 61_89, 8, 9_30, 9, 6]], 'attention_mask': [[1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0], [1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1]]}  # noqa: E501
      # fmt: on

      # camembert is a french model. So we also use french texts.
      a		:   int	  =	[
          'Le transformeur est un modèle d\'apprentissage profond introduit en 2017, '
          'utilisé principalement dans le domaine du traitement automatique des langues (TAL).',
          'À l\'instar des réseaux de neurones récurrents (RNN), les transformeurs sont conçus '
          'pour gérer des données séquentielles, telles que le langage naturel, pour des tâches '
          'telles que la traduction et la synthèse de texte.',
      ]

      self.tokenizer_integration_test_util(
          expected_encoding=__snake_case		,     model_name='camembert-base'		,     revision='3a0641d9a1aeb7e848a74299e7e4c4bca216b4cf'		,     sequences=__snake_case		,     )




'''simple docstring'''






from ...configuration_utils import PretrainedConfig
from ...utils import logging


lowerCAmelCase:   Tuple					=		logging.get_logger(__name__)

lowerCAmelCase:   Any					=		{
    'google/pegasus-large': 'https://huggingface.co/google/pegasus-large/resolve/main/config.json',
    # See all PEGASUS models at https://huggingface.co/models?filter=pegasus
}

class 				a__(   lowerCamelCase__      ):
    lowercase__					=       """pegasus"""
    lowercase__					=       ["""past_key_values"""]
    lowercase__					=       {"""num_attention_heads""": """encoder_attention_heads""", """hidden_size""": """d_model"""}







    def __init__( self					:    Union[str, Any]		,     __snake_case					:    Tuple=5_02_65		,     __snake_case					:    List[Any]=10_24		,     __snake_case					:    Optional[Any]=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    str=16		,     __snake_case					:    int=12		,     __snake_case					:    Tuple=40_96		,     __snake_case					:    int=16		,     __snake_case					:    Tuple=0.0		,     __snake_case					:    Any=0.0		,     __snake_case					:    Any=True		,     __snake_case					:    Optional[int]=True		,     __snake_case					:    Any="gelu"		,     __snake_case					:    str=10_24		,     __snake_case					:    Dict=0.1		,     __snake_case					:    List[Any]=0.0		,     __snake_case					:    str=0.0		,     __snake_case					:    str=0.02		,     __snake_case					:    Union[str, Any]=0		,     __snake_case					:    List[Any]=False		,     __snake_case					:    Optional[Any]=0		,     __snake_case					:    str=1		,     __snake_case					:    Any=1		,     **__snake_case					:    int		,     ):
      a		:   Optional[int]	  =	vocab_size
      a		:   List[str]	  =	max_position_embeddings
      a		:   List[Any]	  =	d_model
      a		:   Tuple	  =	encoder_ffn_dim
      a		:   List[str]	  =	encoder_layers
      a		:   str	  =	encoder_attention_heads
      a		:   str	  =	decoder_ffn_dim
      a		:   Optional[int]	  =	decoder_layers
      a		:   Optional[int]	  =	decoder_attention_heads
      a		:   Optional[int]	  =	dropout
      a		:   Any	  =	attention_dropout
      a		:   str	  =	activation_dropout
      a		:   List[Any]	  =	activation_function
      a		:   Union[str, Any]	  =	init_std
      a		:   List[Any]	  =	encoder_layerdrop
      a		:   Union[str, Any]	  =	decoder_layerdrop
      a		:   List[Any]	  =	use_cache
      a		:   Any	  =	encoder_layers
      a		:   Optional[int]	  =	scale_embedding  # scale factor will be sqrt(d_model) if True
      super().__init__(
          pad_token_id=__snake_case		,     eos_token_id=__snake_case		,     is_encoder_decoder=__snake_case		,     decoder_start_token_id=__snake_case		,     forced_eos_token_id=__snake_case		,     **__snake_case		,     )







    @property
    def      lowercase_			( self					:    int      ):
      return self.encoder_attention_heads







    @property
    def      lowercase_			( self					:    int      ):
      return self.d_model